Master Thesis

Teaching basic cloth manipulation skills

Supervisor/s

Information

If you are interested in the proposal, please contact with the supervisors.

Description

Robotic manipulation of cloth is a challenging and trendy research topic. The challenges come from the high deformability of such material and the difficulties of characterizing the states of objects with infinite degrees of freedom. The interest of facing this topic comes from its relevance both in the clothing and fashion industry, as well in the domestic environment. Despite the huge range of possible tasks and applications, there are a limited number of basic skills that a robot has to acquire for successful cloth manipulation. Learning from Demonstration is a robot programming paradigm based on the execution of tasks before the robot and allows the system to acquire the significant features of the skill, such that they can be later executed under varying conditions.

Objective

The objective of this project is to provide a robotic system with the capability of learning basic cloth manipulation skills from demonstrations and to build up a repertoire of such skills to be used as basic operations of more complex tasks.

Methodology

This project requires to address the following topics:

Define a collection of basic skills like grasping, unfolding, extending, folding,... Identify the significant features of such skills.

Determine the most suited learning algorithms for these skills that are neither simple trajectories or gestures, nor high-level tasks. Hidden Markov Models and Gaussian Mixture Models have been successfully used in the past for similar problems, but a prospective study reviewing the state-of-the-art is required.

Characterize the different states of clothing items such that the correct execution of the skill can be inferred from sensorial information.

Build a repertoire of cloth manipulation skills such that they can be easily retrieved and eventually combined for the execution of more complex tasks.